Economic Evaluation of Extended-Release Buprenorphine for Persons With Opioid Use Disorder

Key Points Question When considering extended-release buprenorphine or transmucosal buprenorphine for treatment for opioid use disorder, which therapy provides the best value for money? Findings This economic evaluation, which used a state transition model to simulate a population with opioid use disorder and included cohorts of 100 000 simulated individuals in each category of intervention, found that when compared with no medication treatment, treatment with transmucosal buprenorphine yielded an incremental cost-effectiveness ratio of $19 740 quality-adjusted life-years gained. In comparison, treatment with extended-release buprenorphine yielded lower effectiveness by 0.03 quality-adjusted life-years per person at higher cost, suggesting that treatment with extended-release buprenorphine was dominated and therefore not preferred. Meaning These results suggest that at current medication cost and retention rates, extended-release buprenorphine was not associated with efficient allocation of limited resources when transmucosal buprenorphine was available; future initiatives should aim to improve retention rates and decrease cost.

The population dynamics modules simulate the epidemiology and demography of the opioid epidemic.The user can create either an open or a closed cohort simulation.In an open cohort simulation, new population "arrives" to the simulation in every time step such that the total population in the model always reflects the size of the total population with OUD living in that jurisdiction.The arrival rate represents both the development of new opioid use disorder and migration into the state among those with existing opioid use.In a closed cohort, no cohort members enter the simulation and the size of the population in the simulation dwindles over time as cohort members die.
The core simulation (Figure 1) of the RESPOND model involves the simulation of the natural history of opioid use disorder as a relapsing and remitting disease over a lifetime.RESPOND simulates OUD as a series of transitions between four health states of opioid use: 1) active, non-injection, 2) non-active, non-injection, 3) active injection, and 4) non-active, injection opioid use.In each time-step of the simulation, population fractions move between opioid use states.The definitions of "active" and "injection" opioid use can vary (but must be pre-specified) depending on the users' needs and available information.In the RESPOND Massachusetts base case, "active" opioid use is defined as any reported use in the previous seven days."Injection" opioid use reflects any injection in the preceding seven days (a person who is both injecting and using oral opioids would be categorized as "injection" in RESPOND).
The care delivery module (Figure 2) of RESPOND simulates OUD treatment and the core module includes four treatment types: 1) outpatient transmucosal buprenorphine (T-MUCOS-BUP), 2) outpatient extended-release buprenorphine (XR-BUP) 3) outpatient injectable naltrexone (naltrexone), 4) outpatient methadone (methadone) maintenance, and 5) inpatient acute drug detoxification (detox).The model is adaptable to additional intervention types to better reflect local conditions and evolutions in the treatment field.In general, treatment episodes tend to decrease movement into active drug use, increase movement into non-active drug use, and have an independent effect on overdose rates conditional on active drug use.When population disengages from a treatment and is lost to follow-up, those people enter a corresponding "post-treatment state".
The post-treatment state is a fixed interval during which relapse to active drug use is high, tolerance to opioids is lower than before treatment, and the risk of drug overdose among those actively using opioids is higher than it is in the no treatment state.The post-treatment state represents the period of vulnerability and excess overdose observed in real-world settings among patients who have recently relapsed to opioid use after a period of sustained abstinence.
The mortality module simulates both drug-related and competing risks deaths.RESPOND simulates overdose mortality by first simulating overdose incidence as a function of age and type of drug use (injection vs. noninjection use).Next, the model simulates a probability of death conditional on having had an opioid overdose.
The model simulates competing causes of death through the use of standardized mortality ratios that are a function of age, sex, and type of opioid use (injection vs. non-injection).
The primary model outputs are: 1) All-cause mortality, 2) Overdose mortality, and 3) Number of people on treatment.
The simulation process is as follows: At simulation start, the model initiates a cohort of people currently living with OUD in the jurisdiction of interest.Based on data from that jurisdiction, the model assigns the current population to a drug use state, as well as a treatment block, such that the simulated population, including the prevalence of OUD treatment, reflects the status quo.Moving forward through simulated time, the sequence of simulation steps are: 1) aging of the population, 2) arrival of new population, 3) transition between OUD drug use states, 4) transitions into and out of treatment, 5) overdose, and 6) death.At the end of this sequence of processes, the model advances simulated time by one cycle (week) and repeats the process.The simulation continues until a time horizon assigned by the user.

C.1.1 Massachusetts Public Health Data Repository
The Massachusetts Public Health Data Repository (MA PHD) is a linked longitudinal records dataset that includes administrative records and billing claims from over 29 sources.The spine of the database is the Massachusetts All Payers Claims database, which includes medical billing for all payers in the state.The database links personlevel records from vital statistics, the Department of Corrections, Emergency Medical Services, and the Bureau of Substance Addiction Services, such that it is possible to construct longitudinal, person-level trajectories across various treatment episodes, admissions to the hospital, and overdose events [3] RESPOND uses the MA PHD data set to estimate parameters such as OUD epidemiology in MA and rates of transition onto treatments assuming the status quo.

C.1.2 NIDA Clinical Trial Network Protocols 0051 (CTN)
The National Institute on Drug Abuse administers a large clinical trials network for evaluation of treatments for substance use disorders.The Clinical Trial Network (CTN) 0051 protocol was a head-to-head comparative effectiveness trial of sublingual buprenorphine and injectable naltrexone for individuals with opioid use disorder who were accessing acute opioid detoxification services.RESPOND uses urine toxicology data from the trial to estimate transitions between substance use states while taking buprenorphine or naltrexone, as well as health care utilization among patients with OUD [1].[4][5][6]

C.1.3 Medical Literature
In addition to the primary data sources listed above, RESPOND estimates many model parameters from the medical literature.The detailed explanation of model parameters below provides references to the relevant publications.

C.2.1.1 Initial Cohort
To simulate the demography and OUD epidemiology in the underlying population, RESPOND requires the initial cohort to be specified as follows: 1) age and sex distributions of people with opioid use disorder, 2) proportion of people beginning in each drug-use state, and 3) proportion of people within each treatment episode.

Structural Assumptions:
• No population begins the simulation in a post-treatment block.
• RESPOND does not characterize the population by race or ethnicity.

Methodological Notes:
Error! Reference source not found.presents the key parameters related to cohort initialization.
To estimate the prevalence of both identified and unidentified OUD in Massachusetts, we applied a capturerecapture methodology to the Massachusetts Public Health Data Repository (MA PHD) to estimate the prevalence of OUD in Massachusetts during calendar years 2012 to 2015 stratified by age and sex [7].The capture-recapture approach provides a method to estimate the total population with high-risk opioid use, including those who have not been identified as a person who uses opioids and do not appear in medical claims or prevalence surveys.To make population estimates beyond 2015, we combined data sources to extend the MA PHD.First, we used the National Survey on Drug Use and Health (NSDUH) to estimate the longitudinal trend in OUD in the United States 2015-2018.Next, we applied the trend line from NSDUH to the MA PHD OUD prevalence measurements.This approach assumes that the trend in prevalence in MA is similar to the trend nationally, but it provides an estimate of total population count that is informed by MA data.

C.2.2 Natural History of OUD
RESPOND simulates opioid use as a series of transitions through four opioid use health states: 1) Non-active and 2) Active non-injection use, as well as 3) Non-active and 4) Active injection use (Figure 1).Throughout the simulation, there is a multi-directional movement between OUD states.
Transitions between drug use compartments impact four important outcomes: 1) risk of overdose, 2) risk of death from competing causes, 3) health care utilization (cost), and 4) quality of life.
The primary sources of data for substance use transitions are studies from the medical literature.
Structural Assumptions: • OUD is a remitting and relapsing process over a lifetime.There is no health state of OUD cure or permanent recovery.
• Transitions between OUD health states are not time updated.

Methodological Notes:
Table 2 presents the key parameters related to OUD transitions for no treatment.
• CIs for proportions pE, pF, pB, pH, and pD are calculated using the normal approximation to binomial proportions.
• CIs for rates RA, RC, and RD are provided from the manuscript and calculated assuming Poisson distribution.
• Weekly rates and proportions, calculated from the respective overall estimates, are converted to weekly transition probabilities.• The denominators for calculating weekly probabilities depend on whether the respective available proportion or rate estimates are yearly or monthly.

C.2.3 Care Delivery
RESPOND models OUD while engaged with treatment using the same 4-state opioid use simulation that it uses to model OUD without treatment.The 4-state OUD simulation is embedded within all treatment episodes (blocks), such that individuals may both remain engaged with treatment, but also experience periods of drug use relapse.Each treatment type has its own bi-directional transition probabilities between active and non-active use.The net movement between active and non-active use while engaged with treatment favors movement to non-active use over time.
RESPOND simulates treatment using the following parameters: 1. Probability of movement onto treatment from no treatment 2. Treatment initiation effectthe probability of ceasing active opioid use immediately after initiating treatment 3. Bi-directional movements between active and non-active opioid use while engaged with treatment

Probability of loss to follow-up
The population that is lost to follow-up (disengages from care) must pass through a "post-treatment period" before rejoining the simulation of OUD.The post-treatment period is a four-week time, immediately following discontinuation of a treatment, during which the risk of relapse to drug use is high, as is the risk of overdose.
Population that survives the post-treatment period transitions back to the simulation of OUD without treatment.

C.2.3.1 Movement From 'no Treatment' to Treatment Episodes
Structural Assumptions: • Only population in active opioid use states seeks OUD treatment.Population that is not currently using opioids does not seek treatment.
The main source of data to inform the probability of transition from 'no treatment' to a treatment episode is the MA PHD.

Methodological Notes:
The weekly transition probability from 'no treatment' to treatment is calculated as: where N ̂Obs,NoTrt → Trt .q: the observed number of people with OUD who transitioned from no-treatment to treatment q in January 2013 N ̂Total,NoTrt → Trt .q: the total number of people with OUD "at risk" of transitioning from no-treatment to treatment q in January 2013 The weekly transition probability P ̂NoTrt → Trt .q is estimated using data from the MA PHD repository1 , and is stratified by age (16 groups: 5-year age-groups from 10-85, and >85years old), sex, and treatment (q= Detox, Residential, Mmt, Ntx, and Bup) (Table 3).

C.2.3.2 Treatment Initiation Effect
When population begins a treatment for opioid use disorder, for example outpatient transmucosal buprenorphine (T-MUCOS-BUP), a portion of the population immediately transitions from active to non-active use.Following that initial "treatment initiation effect" there is bidirectional movement between active and nonactive use states, even while engaged with treatment.The main source of data for the treatment initiation effect and for substance use transitions while engaged with T-MUCOS-BUP, outpatient extended-release buprenorphine (XR-BUP), outpatient naltrexone (naltrexone,) or outpatient methadone (methadone) is the NIDA CTN urine toxicology data.The CTN trials collected routine periodic urine toxicology from all participants.While the published clinical trials results censored participants at the first relapse to drug use (the primary outcome of that trial), the trials continued to collect data from patients who experienced a relapse, such that the database includes longitudinal urine toxicology from patients who relapsed to active use, as well as some who remitted back to non-active use over the course of the trial.We analyzed those data in an "as treated" manner, such that RESPOND estimates realistic movements between active and non-active drug use states among people who are taking a medication.Note that relapsing to active drug use is not the same as loss to follow-up from treatment (see below).

Methodological Notes:
Upon entering treatment, a proportion of the population immediately transitions from active to non-active opioid use.This proportion p ̂Init_Act→NonAct is stratified by treatment episode as follows: © 2023 Flam-Ross JM et al.JAMA Network Open.
• T-MUCOS-BUP and XR-BUP: 0.74, based on the proportion of observed negative (non-active) urine samples at week 1 • Naltrexone: 0.90, based on the proportion of observed negative urine samples at week 5 • Methadone: 0.57, based on the proportion of observed negative urine samples at week 5 We assume a binomial distribution and we use the Wald's method to calculate 95% CIs for the proportion p ̂Act→NonAct representing the block initiation effect.-T-MUCOS-BUP: transmucosal buprenorphine -XR-BUP: extended-release buprenorphine *These estimates are the same for T-MUCOS-BUP, XR-BUP, naltrexone, methadone, and detox.
** There is no block initiation effect for population that is not currently using opioids, because only population that is currently using opioids seeks care in the model.
Structural Assumptions: • The population engaged with treatment may move between active and non-active opioid use, but the population engaged with treatment does not change the route of administration of their opioid use.In other words, population that entered treatment using non-injection opioids will not escalate to injection drug use while still engaged with treatment (Core Simulation within OUD treatment episodes (blocks) - Transition probabilities between active and non-active states are the same for both injection and non-injection drug use.This structural assumption is confirmed by the MSM estimates for buprenorphine and methadone models, in which route was included as a model covariate, but was not a significant predictor of transition rates Methodological Notes: • Each MSM includes age and sex as covariates.
• Age is included as a continuous covariate in the MSM model, thus allowing estimation of the transition probabilities for age bins in which data are not available.We consider five 5 age groups: 10-19, 20-24, 25-34, 35-49, and 50-99 years old.
• OUD transition for T-MUCOS-BUP, XR-BUP, and methadone: We keep all the weekly MSM estimates of OUD transition probabilities except week 1, which is considered as block initiation.
• OUD transition for naltrexone: We delete the estimates for the first 4 weeks due to the inaccurate results from detoxification.Week 5 is also excluded from the analysis, as it is considered as block initiation.
• Transition probabilities from non-active to active use are defined as: p ̂Trt_NonAct→Act = 1 − p ̂Trt_Act→NonAct

C.2.3.4 Probability of Loss to Follow-Up
In every time step, the population that is engaged with treatment faces a risk of disengaging from care and being lost to follow-up.Loss to follow-up differs from relapse to active drug use while remaining engaged with opioid treatment.The population that disengages with care and is lost to follow-up enters the "post-treatment state," during which time individuals have a high rate of relapse to active use and a high rate of overdose among active users.The post-treatment block represents the period of time immediately following discontinuation of a medication or release from an abstinence-based setting (acute drug detoxification center, residential drug treatment, or jail), when opioid tolerance is low and the risk of overdose is higher than that of a person who never initiated treatment.
The main source of data for estimating the probability of loss to follow-up is the IBM Watson MarketScan© Commercial Claims Database, a large insurance claims database containing millions of individuals who have commercial insurance coverage.As a randomized controlled trial, the CTN data cannot provide estimates of retention in care or loss to follow-up in the real world.We have previously published rates of loss to follow-up from buprenorphine and naltrexone treatment [14].We therefore turn to MarketScan© which is nationally representative and reflects real-world practice in the U.S.

Structural Assumptions:
• In RESPOND, the only way to transition into a post-treatment episode is from a corresponding treatment episode.
• The "no Treatment" block does not have a post-treatment episode.
• RESPOND also considers the probability of immediate relapse to active opioid use upon being lost to follow-up from treatment: Methodological Notes: The weekly transition probability from treatment to post-treatment is calculated as:
Table 6 presents estimates of the weekly transition probabilities P ̂Trt → Post−Trt .qbased on data from Morgan et al. [14], stratified by treatment.

C.2.4 Overdose
Every person who is actively using opioids faces the risk of overdose.The probability of overdose depends on age, sex, and route of drug use (injection vs. non-injection).The simulation has no memory of past overdose events and does not include an elevated risk of repeat overdose after experiencing a first overdose event.

Structural Assumptions:
• Experiencing overdose has no independent impact on current or future opioid use behaviors.
• Only the population that is in an active opioid use state faces the risk of overdose.
• The risk of overdose is different between no treatment, treatment, and post-treatment episodes.
• The risk of overdose is lower while engaged in treatment compared to not engaged, even among the population who are actively using drugs while engaged with treatment.

Methodological Notes:
Counts of overdose are a target for model calibration.

C.2.4.1 No Treatment
The rate ROD,t of overdose at time t for people not engaged in treatment is calculated as: for years t= 2015, assuming that each person contributes 1 person/year where NOD,t: number overdose cases at time t NOUD,t: OUD cohort size at time t Nenter,t: entering cohort size at time t Overdose probabilities POD,t are calculated from the respective overdose rates as: Table 8 presents point estimates and 95% CIs of the overdose rates by age group, sex, OUD type, and year.We calculate 95% CIs for these rates assuming that the respective overdose counts NOD,t follow a Poisson distribution and using the normal approximation.

C.2.4.2 Overdose While on Treatment
The risk of overdose for people engaged in treatment, is derived by applying a multiplier parameter mOD,q to the respective no treatment ROD,t estimates.i.e., the weekly overdose rate at time t for treatment q is: ROD,t,q = mOD,q  ROD,t where ROD,t is the no treatment overdose rate at time t.[16] Abbreviations: -T-MUCOS-BUP: transmucosal buprenorphine -XR-BUP: extended-release buprenorphine

C.2.4.3 Overdose During the Post-Treatment Period
During the post-treatment period, individuals face a risk of overdose higher than that of people who never initiated a treatment.
The post-treatment rates of overdose at time t are calculated as: where RFOD,t and PFOD,t are the post-detoxification fatal overdose rates and proportions respectively at time t (t=2015), Pact_inj is the proportion of active injectors, and 1-Pact_inj is the proportion of active non-injectors.We assume P act_inj = 0.329.

C.2.5.1 Fatal Overdose
The population that experiences overdose then faces a probability of death conditional on having had an overdose.This conditional probability of death, given an opioid overdose, is generalizable to all overdose cases and is therefore not stratified by age, sex, or OUD status.The population that survives an overdose does not change substance use as a result of the overdose.The probability of death conditional on having experienced an overdose is a time updated variable, reflecting changes to drug supply over time.Adjusting the conditional probability of overdose death provides a mechanism to reflect the growing penetration of fentanyl in local drug supplies, which is a major dynamic underlying mounting overdose deaths in the U.S.
The probability PFOD,t of fatal overdose at year t is calculated as: where NFOD,t is the total number of fatal overdoses, and NOD,t is the total number of all-type overdoses.

C.2.5.2 Competing risks of death (non-overdose mortality)
Competing risks mortality includes deaths from conditions such as infectious endocarditis and sepsis, as well as medical comorbidities that accrue over a lifetime.The general approach to estimating competing risks of death is to apply standardized mortality ratios (SMRs) reflecting elevated mortality among drug users to age-sex stratified actuarial lifetables for the U.S.
The SMR is calculated as: where ND_other is the number of observed deaths not due to opioid overdose, RD is the census death rate, and NOUD is the size of the OUD population based on chapter 55 estimation.
We construct CIs around the SMRs estimates assuming that the ND_other follows a Poisson distribution and using the normal approximation (Table 12).

C.2.5.3 Summary of the combined impact of medications for opioid use disorder on all-cause mortality in RESPOND
Medications for opioid use disorder (MOUD) have two independent effects on mortality that combine to provide synergies in the simulation: 1.The population that is engaged with MOUD treatment experiences a net movement toward non-active drug use.Because there is no risk of overdose while not using drugs, MOUD tend to decrease the rate of overdose in the population.In addition, movement out of active drug use states reduces exposure to the high standardized mortality ratios (SMRs) of active drug use and thereby reduce non-overdose mortality as well.2. Among those who are actively using drugs when taking an MOUD, the MOUD has an independent effect on overdose risk, such that even those who are using have lower risk of death than those who are using drugs while not engaged with MOUD treatment.

D. COSTS AND UTILITIES
The primary data source for costing is the NIDA CTN-051 trial.

4 PG
Proportion of non-active injection to active injectionPCProbability of non-active non-injection to active non-injection 0.2308 Calculated from p: 1-exp(x) where x = ln(1-p)/Probability of non-active injection to active injection • p * C and p * G indicate the percentage of people relapsed within a month of discharge (after inpatient detoxification).

c
Figure legend: This figure represents the change in value of the incremental cost-effectiveness ratio (ICER) when the parameter value is varied 20% above and below its base case value.The light blue color represents a 20% decrease in the value and the dark blue color represents a 20% increase in the value.

Table 2 . No-Treatment: Opioid Use Disorder Transition Parameters Parameter Description Value Method Source No Treatment RA
pF Proportion of non-active non-injection to active injection PE Probability of non-active injection to active non-Same estimates with no-treatment.

Table 4 . Block Initiation Effects Parameters: Weekly Transition Probabilities Modeling Movement Between OUD States When Movement Between Treatment States Occurs Initial OUD state
Abbreviations:

Table 7 . Empirically Opioid Overdoses in MA = calibration targets for the model
Table 7 provides the empirically observed overdose fatalities from MA PHD.

Table 10
presents the fatal overdose rates and probabilities by age group and sex.

Table 14 .
Utility estimates for drug use states and treatment blocks.RESPOND model validation to Massachusetts fatal opioid overdose counts, as reported in the MA Governor's Report (https://www.mass.gov/doc/opioid-related-overdose-deaths-among-ma-residents-may-2021/download).